The following commentary can also be viewed and commented upon on the Supply Chain Expert Community web site.

Fellow supply chain expert community blogger Lora Cecere has started a Big Data Supply Chains dialogue on her Supply Chain Shaman blog.  Lora points out that supply chain “data volumes are exploding, data velocity is increasing and data types are proliferating.” Lora makes the argument that organizations increasingly need to embrace the concept of “Big Data Supply Chains” which are defined as “value networks that extend from the customer’s customer to the supplier’s supplier, that sense, shape and respond by listening, testing and learning with minimal latency.” Lora advocates that Big Data Supply Chains will transform Advanced Planning and Scheduling (APS) as well as redefine CRM and SRM applications. Some, such as ourselves, point to this new area as being the foundation for predictive analytics or supply chain cockpit capabilities. They are in essence, the next frontier for enabling smarter and more informed decision making in S&OP and other enterprise management processes.

Supply Chain Matters read a recent report published by Accenture that makes some rather important observations regarding the direction of predictive analytics.  Many of today’s business warehouse or business intelligence applications are built with design principles of data being attached to particular applications. For instance, SAP installed base customers are well aware that individual SAP applications feed data to SAP Business Warehouse (BW), and when applications such as SAP APO (Advanced Planning and Optimization) require more-timely data intensive reporting, a condensed copy of BW is actually affixed to the application.  Accenture points out those new data platform architectures will be selected primarily to cope with soaring volumes of data along with the complexity of data management, in effect, data de-coupling from individual applications.  The Accenture paper further advocates for streaming databases that include distributed ownership and control of data, not just the physical storing of data in different application silos and data centers.

In her commentary, Lora rightfully outlines some of the significant challenges involved towards achieving this concept. While these new approaches have the potential to allow the supply chain to “learn and predict”, they do present challenges for gaining executive level investment support, especially the CFO, not to mention the CIO who has to deal with the consequences of exploding data eating up IT infrastructure. That particular tenant is one we feel is the most important, since without executive level leadership and sponsorship, many IT initiatives have little chance of success. Also, as many in our community know, previous multi-year ERP implementation that ended up consuming far more management time and costing too much money have left a sour taste for technology leapfrog. The principles of predictive analytics imply that various supply chain functional teams will need to have much deeper skills in data management, trading partner collaboration and analytics disciplines. It further implies that trading partners and customers will be comfortable with sharing of sensitive data. There are also strong implications for some organizational centralization of analytics teams.

In our view, all of these factors point to fairly significant change management. Change does not occur until and unless organizational motivators for change exist. We continue to believe that success, for the business and for customers and suppliers, are always the best catalyst for change, especially in the current volatile and uncertain business environment.

Instead, why not channel big data challenges into baby step initiatives aimed at a portfolio at information hubs augmented with predictive analytics competencies. Consider pilot programs targeted at specific problems in demand sensing, supply risk, or logistics and distribution orchestration.

Big data supply chains” are indeed overwhelming organizational and physical resources, adding more challenge to the needs for more timely and market responsive decision-making.   Work closely with IT, business and trading teams and channel the frustration toward a new framework of data architecture and predictive analytics capabilities.

Consider that if we are thinking of doing a major renovation of our homes, and we do not understand all that is involved, we often do some homework, seek knowledge from experts, set a reasonable budget and timeline and gain the support of fellow family members.  This same analogy can be applied to channeling the frustration of drowning in data into the harvesting of predictive supply chain capabilities. Walk before you run and take steps that bring teams to initial successes along the journey.

Bob Ferrari